Prediction and compensation of subsequent deformation in robotbased incremental sheet forming by application of machine learning

板料渐进成形（ISF）是一种灵活的、与工件无关的小批量钣金零件制造工艺。由于工艺的几何精度仍然很低，工业应用还很少或没有全部发生。其原因主要是缺少精确模拟成形过程的可能性，阻碍了回弹和后续变形补偿方法的使用。虽然有多种基于FEM的仿真方法，但由于过程的增量性质导致的仿真误差的总和，阻止了它们的应用。在研究项目期间，将通过使用机器学习来追求数据驱动的方法，与FEM模拟相比，机器学习不需要对成形过程进行详细建模。建立了一个多层人工神经网络（ANN），根据常用的工艺参数、零件几何形状和刀具轨迹，预测成形实验的几何精度。为了能够训练ANN，建立了一个工艺数据库。将在实验系列中获得优选宽范围的过程数据，其中将形成系统变化的部件并利用交替的过程参数进行测量。通过这种方式生成的训练数据范围广泛，使人工神经网络的泛化，使其适用于任何部分。考虑到部分几何形状对所得几何精度的影响，部分几何形状将被转换为具有固定数量的参数，因此可用于机器学习的格式。这是通过开发几种表示方法来实现的，这些方法的性能是用质量标准来评估的。这评估了几何表示和零件几何参数的近似和相关性的质量。利用建立的工艺数据库和开发的具有最高性能的几何表示，将训练多层ANN。同时，通过参考成形实验中收集的测试数据集验证了该方法的预测性能。然后，训练好的神经网络用于提高零件的几何精度，通过修改刀具路径的基础上预测的几何精度。为了执行这一点，现有的刀具路径规划方法需要扩展，因为ANN的数据驱动性质可能导致罕见的预测错误，否则会导致错误的刀具路径。

Incremental sheet forming (ISF) is flexible, workpiece-independent process for manufacturing sheet metal parts in small lot sizes. An industrial application has yet rare or not all taken place due to the still low geometric accuracy of the process. The reason for this is mainly the missing possibility for a precise simulation of the forming process, hindering the use of compensation approaches for springback and subsequent deformation. While there are multiple FEM-based simulation approaches, their application is prevented by summing up simulation errors, caused by the incremental nature of the process. During the research project, a data driven approach will be pursued by the usage of machine learning, which, in contrast to FEM-simulations, does not need a detailed modelling of the forming process. A multi-layer artificial neural network (ANN) will be build up, predicting the resulting geometric accuracy of a forming experiment based on common process parameters, part geometry and the course of the tool path.To be able to train the ANN, a process database will be built up. A preferably wide spectrum of process data will be acquired in an experimental series, in which a systematically varied part will be formed and measured with alternated process parameters. By the this way generated wide range of the training data, a generalisation of the ANN is enabled making it applicable to any part.To take the influence of the part geometry on the resulting geometric accuracy into account, the part geometry will be transformed into a format with a fixed number of parameters which is therefore usable for machine learning. This is achieved by the development of several representation approaches whose performances are evaluated with a quality criterion. This assesses the quality of the approximation and correlation of the parameters of the geometry representation and the part geometry.Utilizing the built up process database and the developed geometry representation with the highest performance, a multi-layer ANN will be trained. Meanwhile its prediction performance is validated with a test dataset gathered in reference forming experiments. Afterwards the trained ANN is used to improve the geometric accuracy of a part by modifying the tool path based on the predicted geometric accuracy. To execute this, existing tool path planning approaches need to be extended as the data driven nature of the ANN can lead to rare prediction errors otherwise resulting in false tool paths.